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ferro-ta-main/benchmarks/data_generator.py

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+"""
+Benchmark data generator for cross-library comparison.
+
+Produces C-contiguous float64 NumPy arrays that work correctly with all
+six libraries (ferro-ta, TA-Lib, pandas-ta, ta, Tulipy, finta).
+Critical: every array is np.ascontiguousarray(..., dtype=np.float64) to
+prevent memory segmentation faults in C-extension libraries.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+_RNG = np.random.default_rng(42)
+
+
+def generate_ohlcv(size: int = 10_000) -> dict[str, np.ndarray]:
+    """Return a dict of C-contiguous float64 OHLCV arrays.
+
+    Uses a geometric Brownian motion walk so values are realistic (no
+    negatives, bounded intraday spread).  Every array satisfies:
+      high >= close >= low > 0
+      open > 0
+      volume > 0
+    """
+    # Geometric random walk for close
+    returns = _RNG.normal(0.0002, 0.01, size)
+    close = 100.0 * np.exp(np.cumsum(returns))
+
+    noise_hi = np.abs(_RNG.normal(0, 0.005, size)) * close
+    noise_lo = np.abs(_RNG.normal(0, 0.005, size)) * close
+
+    high = close + noise_hi
+    low = np.maximum(close - noise_lo, 0.01)  # never negative
+    open_ = low + _RNG.random(size) * (high - low)
+    volume = _RNG.uniform(1e5, 1e7, size)
+
+    def _c(arr: np.ndarray) -> np.ndarray:
+        return np.ascontiguousarray(arr, dtype=np.float64)
+
+    return {
+        "open": _c(open_),
+        "high": _c(high),
+        "low": _c(low),
+        "close": _c(close),
+        "volume": _c(volume),
+    }
+
+
+def get_pandas_ohlcv(data: dict[str, np.ndarray]) -> pd.DataFrame:
+    """Convert an OHLCV dict to a DataFrame with a DatetimeIndex.
+
+    pandas-ta and finta both require a datetime-indexed DataFrame with
+    lowercase column names (open/high/low/close/volume).
+    """
+    idx = pd.date_range("2015-01-01", periods=len(data["close"]), freq="D")
+    return pd.DataFrame(data, index=idx)
+
+
+# Pre-built datasets at several scales so benchmarks can import them directly
+SMALL = generate_ohlcv(1_000)
+MEDIUM = generate_ohlcv(10_000)
+LARGE = generate_ohlcv(100_000)
+
+SMALL_DF = get_pandas_ohlcv(SMALL)
+MEDIUM_DF = get_pandas_ohlcv(MEDIUM)
+LARGE_DF = get_pandas_ohlcv(LARGE)